We presented a novel approach for Invasive Ductal Carcinoma cell discrimination in histopathology slides. The model is composed of Inception variant blocks with combined batch normalization steps to reduce internal covariances. Results surpass state of the art classification methods which were trained and tested on the same dataset. The quantitative evaluation of the results was carried out through balanced accuracy and F1-score, metrics used for unbalanced datasets. Future work will be focused on the inclusion of residual blocks [1]} to deal with the vanishing gradients during the training, which in theory will lead to improved accuracy.
